Apache Spark for Java Developers
What you will learn
Use functional style Java to define complex data processing jobs
Learn the differences between the RDD and DataFrame APIs
Use an SQL style syntax to produce reports against Big Data sets
Use Machine Learning Algorithms with Big Data and SparkML
Connect Spark to Apache Kafka to process Streams of Big Data
See how Structured Streaming can be used to build pipelines with Kafka
Section 1: Introduction
Section 2: Getting Started
Section 3: Reduces on RDDs
Section 4: Mapping and Outputting
Section 5: Tuples
Section 6: PairRDDs
Section 7: FlatMaps and Filters
Section 8: Reading from Disk
Section 9: Keyword Ranking Practical
Section 10: Sorts and Coalesce
Section 11: Deploying to AWS EMR (Optional)
Section 12: Joins
Section 13: Big Data Big Exercise
Section 14: RDD Performance
Section 15: Module 2 - Chapter 1 SparkSQL Introduction
Section 16: SparkSQL Getting Started
Section 17: Datasets
Section 18: The Full SQL Syntax
Section 19: In Memory Data
Section 20: Groupings and Aggregations
Section 21: Date Formatting
Section 22: Multiple Groupings
Section 23: Ordering
Section 24: DataFrames API
Section 25: Pivot Tables
Section 26: More Aggregations
Section 27: Practical Exercise
Section 28: User Defined Functions
Section 29: SparkSQL Performance
Section 30: HashAggregation
Section 31: SparkSQL Performance vs RDDs
Section 32: Module 3 - SparkML for Machine Learning
Section 33: Linear Regression Models
Section 34: Training Data
Section 35: Model Fitting Parameters
Section 36: Feature Selection
Section 37: Non-Numeric Data
Section 38: Pipelines
Section 39: Case Study
Section 40: Logistic Regression
Section 41: Decision Trees
Section 42: K Means Clustering
Section 43: Recommender Systems
Section 44: Module 4 -Spark Streaming and Structured Streaming with Kafka
Section 45: Streaming Chapter 2 - Streaming with Apache Kafka
Get processing Big Data using RDDs, DataFrames, SparkSQL and Machine Learning - and real-time streaming with Kafka!
- Java 8 is required for the course. Spark does not currently support Java9+, and you need Java 8 for the functional Lambda syntax
- Previous knowledge of Java is assumed, but anything above the basics is explained
- Some previous SQL will be useful for part of the course, but if you've never used it before this will be a good first experience
Get started with the amazing Apache Spark parallel computing framework - this course is designed especially for Java Developers.
If you're new to Data Science and want to find out about how massive datasets are processed in parallel, then the Java API for spark is a great way to get started, fast.
All of the fundamentals you need to understand the main operations you can perform in Spark Core, SparkSQL and DataFrames are covered in detail, with easy to follow examples. You'll be able to follow along with all of the examples, and run them on your own local development computer.
Included with the course is a module covering SparkML, an exciting addition to Spark that allows you to apply Machine Learning models to your Big Data! No mathematical experience is necessary!
And finally, there's a full 3 hour module covering Spark Streaming, where you will get hands-on experience of integrating Spark with Apache Kafka to handle real-time big data streams. We use both the DStream and the Structured Streaming APIs.
Optionally, if you have an AWS account, you'll see how to deploy your work to a live EMR (Elastic Map Reduce) hardware cluster. If you're not familiar with AWS you can skip this video, but it's still worthwhile to watch rather than following along with the coding.
You'll be going deep into the internals of Spark and you'll find out how it optimizes your execution plans. We'll be comparing the performance of RDDs vs SparkSQL, and you'll learn about the major performance pitfalls which could save a lot of money for live projects.
Throughout the course, you'll be getting some great practice with Java 8 Lambdas - a great way to learn functional-style Java if you're new to it.
NOTE: Java 8 is required for the course. Spark does not currently support Java9+ (we will update when this changes) and Java 8 is required for the lambda syntax.
Who this course is for:
- Anyone who already knows Java and would like to explore Apache Spark
- Anyone new to Data Science who want a fast way to get started, without learning Python, Scala or R!